PARTICLE SWARM OPTIMIZATION METHOD FOR IMAGE CLUSTERING

2005 ◽  
Vol 19 (03) ◽  
pp. 297-321 ◽  
Author(s):  
M. OMRAN ◽  
A. P. ENGELBRECHT ◽  
A. SALMAN
Keyword(s):  
Satellite Images ◽  
State Of The Art ◽  
Particle Swarm ◽  
Optimization Method ◽  
Image Clustering ◽  
Control Parameters ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Harmonic Means ◽  
Better Than

An image clustering method that is based on the particle swarm optimizer (PSO) is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together with similar image primitives. To illustrate its wide applicability, the proposed image classifier has been applied to synthetic, MRI and satellite images. Experimental results show that the PSO image classifier performs better than state-of-the-art image classifiers (namely, K-means, Fuzzy C-means, K-Harmonic means and Genetic Algorithms) in all measured criteria. The influence of different values of PSO control parameters on performance is also illustrated.

scholarly journals Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Jun Zheng ◽  
Hai-Feng Ling ◽  
Qiu Guan
Keyword(s):  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Method ◽  
Evolutionary Information ◽  
Adaptive Mechanism ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Adaptive Parameters ◽  
Individual Particles

Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.

scholarly journals Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

2018 ◽  
Vol 1 (1) ◽  
pp. 157-191 ◽  
Author(s):  
Saptarshi Sengupta ◽  
Sanchita Basak ◽  
Richard Peters
Keyword(s):  
Particle Swarm Optimization ◽  
State Of The Art ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Fish Schools ◽  
Deterministic Algorithms ◽  
Multidimensional Problems ◽  
Recent Developments

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment, and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

A Robust and Blind 3D Mesh Watermarking Approach Based on Particle Swarm Optimization

2020 ◽  
Vol 12 (1) ◽  
pp. 24-48
Author(s):  
Mourad R. Mouhamed ◽  
Mona Mohamed Soliman ◽  
Ashraf Darwish ◽  
Aboul Ella Hassanien
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
3D Mesh ◽  
Mesh Model ◽  
Points Of Interest ◽  
Mesh Models

This article presents a robust 3D mesh watermarking approach, which adopts an optimization method of selecting watermark vertices for 3D mesh models. The proposed approach can enhance the imperceptibility of the watermarked model without affecting the robustness and capacity factors. The proposed watermark approach depends on an embedding algorithm that use a clustering strategy, based on K−means clustering algorithm in conjunction with the particle swarm optimization to divide the mesh model vertices into groups. Points of interest set (POIs) are selected from these clustered groups and mark it as watermark vertices where the (POIs) are invariant to most of the geometrical and connectivity attacks. Then, the proposed approach inserts the watermark bit stream in the decimal part of spherical coordinates for these selected watermark vertices. The experimental results confirm that the proposed approach proves its superiority compared with state-of-the-art techniques with respect to imperceptibility and robustness.

scholarly journals Effective Image Clustering with Differential Evolution Technique

2011 ◽  
pp. 169-177
Author(s):  
G Sudhakar ◽  
Polinati Vinod Babu ◽  
Suresh Chandra Satapathy ◽  
Gunanidhi Pradhan
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Quality Measures ◽  
Optimization Method ◽  
Population Based ◽  
Image Clustering ◽  
Swarm Optimization ◽  
Novel Approach ◽  
Image Datasets

The paper presents a novel approach of clustering image datasets with differential evolution (DE) technique. The differential evolution is a parallel direct search population based optimization method. From our simulations it is found that DE is able to optimize the quality measures of clusters of image datasets. To claim the superiority of DE based clustering we have compared the outcomes of DE with the classical K-means and popular Particle Swarm Optimization (PSO) algorithms for the same datasets. The comparisons results reveal the suitability of DE for image clustering in all image datasets.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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